Mostly, machine learning (ML) based automation systems are supervised systems, and primarily rely on labeled examples coded by analysts for learning specific tasks, such as labeling. The idea of using ML-based automation systems has led to significant contributions to domain adaptation and transfer learning (DA/TL) techniques. The DA/TL techniques leverage knowledge, such as labeled data, from one or multiple source domains to learn an accurate model for unlabeled data in a target domain.
Advancements in DA/TL techniques are also used in same-domain and cross-domain text classification. Typically, systems that deploy DA/TL techniques for cross-domain text classification work on the assumption that the source data (i.e., the labeled data) and target data (i.e., the unlabeled data) follow the same distribution. However, in practice, this assumption does not hold true due to inconsistency between the sentiments/polarity expressed by the source data and those by the target data. Thus, an advanced technique may be desired that efficiently performs cross-domain classification irrespective of the dissimilarity in data distributions and inconsistency between sentiments/polarity.
Further limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through comparison of described systems with some aspects of the present disclosure, as set forth in the remainder of the present application and with reference to the drawings.